1 . As Frans de Waal, a primatologist (灵长动物学家), recognizes, a better way to think about other creatures would be to ask ourselves how different species have developed different kinds of minds to solve different adaptive problems. Surely the important question is not whether animals can do the same things humans can, but how those animals solve the cognitive (认知的) problems they face, like how to imitate the sea floor. Children and some animals are so interesting not because they are smart like us, but because they are smart in ways we haven’t even considered.
Sometimes studying children’s ways of knowing can cast light on adult-human cognition. Children’s pretend play may help us understand our adult taste for fiction. De Waal’s research provides another interesting example. We human beings tend to think that our social relationships are rooted in our perceptions, beliefs, and desires, and our understanding of the perceptions, beliefs, and desires of others — what psychologists call our “theory of mind.” In the 80s and 90s, developmental psychologists showed that pre-schoolers and even infants understand minds apart from their own. But it was hard to show that other animals did the same. “Theory of mind” became a candidate for the special, uniquely human trick.
Yet de Waal’s studies show that chimps (黑猩猩) possess a remarkably developed political intelligence — they are much interested in figuring out social relationships. It turns out, as de Waal describes, that chimps do infer something about what other chimps see. But experimental studies also suggest that this happens only in a competitive political context. The evolutionary anthropologist (人类学家) Brain Hare and his colleagues gave a junior chimp a choice between pieces of food that a dominant chimp had seen hidden and other pieces it had not seen hidden. The junior chimp, who watched all the hiding, stayed away from the food the dominant chimp had seen, but took the food it hadn’t seen.
Anyone who has gone to an academic conference will recognize that we may be in the same situation. We may say that we sign up because we’re eager to find out what other human beings think, but we’re just as interested in who’s on top. Many of the political judgments we make there don’t have much to do with our theory of mind. We may show our respect to a famous professor even if we have no respect for his ideas.
Until recently, however, there wasn’t much research into how humans develop and employ this kind of political knowledge. It may be that we understand the social world in terms of dominance, like chimps, but we’re just not usually as politically motivated as they are. Instead of asking whether we have a better everyday theory of mind, we might wonder whether they have a better everyday theory of politics.
1. According to the first paragraph, which of the following shows that an animal is smart?| A．It can behave like a human kid. |
| B．It can imitate what human beings do. |
| C．It can find a solution to its own problem. |
| D．It can figure out those adaptive problems. |
| A．We talk with infants in a way that they can fully understand. |
| B．We make guesses at what others think while interacting with them. |
| C．We hide our emotions when we try establishing contact with a stranger. |
| D．We try to understand how kids’ pretend play affects our taste for fiction. |
| A．Neither human nor animals display their preference for dominance. |
| B．Animals living in a competitive political context are smarter. |
| C．Both humans and some animals have political intelligence. |
| D．Humans are more interested in who’s on top than animals. |
| A．we know little about how chimps are politically motivated |
| B．our political knowledge doesn’t always determine how we behave |
| C．our theory of mind might enable us to understand our theory of politics |
| D．more research should be conducted to understand animals’ social world |
2 . Borders, departments, or issue areas all represent what systems analysts call system boundaries. System boundaries divide the big, messy, interconnected world into smaller subsystems. This is useful, even necessary. Our minds and our collective governance systems would be stuck if we had to always consider all the connections of everything to everything else. But dividing systems into subsystems can sometimes break a natural cooperativity. For instance, a decarbonizing country will spend money in its energy and transportation sectors and save money in its health system.
Decarbonization could be a win for the whole, but it might be experienced as a bother for particular subsystems.
Donella Meadows, the early systems modeler, wrote that system boundaries are “lines in the mind, not in the world.” And that is actually good news. If departments, and disciplines are just ideas, then there is nothing immovable about them. We can make these borders less obvious and conduct partnerships across them. We can even redraw them to include more of what matters in a single project or investment. That’s the premise of multisolving — using one investment of time or effort to achieve several goals at once.
For instance, Warm Up New Zealand (WUNZ) upgraded the energy efficiency of residential buildings and provided jobs in the building sector after a financial downturn. The project resulted in better health for residents, as well. That translated into health systems savings. Taken together, a 2011 study estimated that across all these benefits, the project saved $3.90 for every $1 invested.
Multisolving seems possible everywhere and like an obvious choice. Yet, it is very much the exception, not the rule. Why is multisolving still so rare when it has the power to boost progress on some of the most urgent issues we face?
Unfamiliarity stands in the way, as does an often-unexamined assumption that making issues smaller makes them easier to address. We often hear the viewpoint, “I already work on poverty (or climate, etc.) and that’s hard enough. Why should I add biodiversity or pollution to the mix?” Fundraising for crossing borders can be a struggle too. Funders want the “visible results” shown, but they don’t always see crossing borders as an essential part of achieving those results.
It is easy to devalue and underemphasize connection-building. After all, it can be delicate and not always visible. But to realize goals in today’s world, from equitable policies and low-carbon facilities to values like cooperation and fairness, we do need deep shifts, and we need them soon. And facilitating the flow of ideas back and forth across borders is one way to speed change.
1. As for systems boundaries, the author is ______.| A．critical | B．puzzled | C．supportive | D．unconcerned |
| A．Prediction. | B．Precondition. | C．Prevention. | D．Presentation. |
| A．People are familiar with multisolving. |
| B．WUNZ performed multisolving successfully. |
| C．Raising money helps to produce visible results. |
| D．Multisolving is widely used to address problems. |
| A．Multisolving: Hard to achieve soon |
| B．Multisolving: Essential to solve small issues |
| C．Multisolving: Conducting partnership across borders |
| D．Multisolving: Making systems whole, healthy, and sustainable |
3 . Whenever anyone asks me what tech I’d like to see invented, I always say the universal translator, which lets you understand and speak any language.
Meta AI recently announced the start of the universal speech translator (UST) project, which aims to create AI systems that enable real-time speech-to-speech translation across all languages, even those that are spoken but not commonly written. Meta says that today’s AI translation models are focused on widely-used written languages, and that more than 40% of primarily spoken languages are not covered by such translation technologies.
According to Meta, the model is the first AI-powered speech translation system for the unwritten language Hokkien (闽南语), a Chinese language spoken in southeastern China. The system allows Hokkien speakers to hold conversations with English speakers, a significant step toward bringing people together wherever they are located.
To build UST, Meta AI focused on overcoming three important translation system challenges. It addressed data scarcity by getting more training data in more languages and finding new ways to use the data it had found. It solved the modeling problems that arise as models grow to serve many more languages. And it sought new ways to improve on its results.
Meta AI claims that the techniques it pioneered with Hokkien can be extended to many other unwritten languages—and eventually work in real time. For this purpose, Meta has released the Speech Matrix, a large collection of speech-to-speech translations, which enables other research teams to create translation models for other languages.
Artificial (人工的) speech translation could play a significant role in our world. For interactions, it will enable people from around the world to communicate with each other more smoothly, making the social net more interconnected. For content, using artificial speech translation allows you to easily localize content.
Yashar Behzadi, CEO and founder of Synthesis AI, believes that technology needs to enable more natural experiences if the digital world is to succeed. He says that one of the current challenges for UST models is the computationally expensive training that’s needed because of the wide range and very slight differences in meaning or sound of languages. Also, to train strong AI models requires vast amounts of typical data. A significant bottleneck to building these AI models in the near future will be to ensure training data collect the privacy in agreement with rules and law.
1. What is the feature of the UST project?| A．It changes spoken languages to written forms. |
| B．It attracts wider attention to written languages in translation. |
| C．It adds 40% of spoken languages into translation technology. |
| D．It enables real-time speech-to-speech translation across all languages. |
| A．Lack. | B．Mistake. | C．Recovery. | D．Management. |
| A．It is expensive to collect typical data. |
| B．It increases the use of a certain language. |
| C．Its techniques are finally developed for Hokkien. |
| D．It helps inspire interactions and content localization. |
| A．AI Translation: Make Translation Faster |
| B．AI Translation: Meet You in All Languages |
| C．Unwritten Language: Bring People Together |
| D．Unwritten Language: Translation Challenge |
4 . In general, the society is becoming one of giant enterprises directed by a bureaucratic (官僚主义的) management in which man becomes a small, well-oiled cog in the machinery. The oiling is done with higher wages, well-equipped factories and piped music, and by psychologists and “human-relations” experts; yet all this oiling does not change the fact that man has become powerless, that he does not wholeheartedly participate in his work and he is bored with it. In fact, the blue-collar and the white-collar workers have become economic puppets who dance to the tune of automated machines and bureaucratic management.
The worker and employee are anxious, seemingly because they might find themselves out of a job or they would say that they are unable to acquire any real satisfaction or interest in life. In fact, they feel desperate as they live and die without ever having confronted the fundamental realities of human existence as emotionally and intellectually independent and productive human beings.
Those higher up on the social ladder are no less anxious. Their lives are no less empty than those of their subordinates. They are even more insecure in some respects. They are in a highly competitive race. To be promoted or to fall behind is not a matter of salary but even more a matter of self-respect. When they apply for their first job, they are tested for intelligence as well as for the right mixture of submissiveness and independence. From the moment on they are tested again and again by the psychologists, for whom testing is a big business, and by their superiors, who judge their behavior, sociability, capacity to get along, etc. This constant need to prove that one is as good as or better than one’s fellow-competitor creates constant anxiety and stress, the very causes of unhappiness and illness.
Am I suggesting a return to the pre-industrial mode of production or to nineteenth-century “free enterprise” capitalism? Certainly not. Problems are never solved by returning to a stage which one has already outgrown. I suggest transforming the social system from a bureaucratically managed industrialism in which maximal production and consumption are ends in themselves into a humanist industrialism in which man and full development of his potentialities — those of all love and of reason — are the aims of social arrangements. Production and consumption should serve as means to this end, and should be prevented from ruling man.
1. By “a small, well-oiled cog in the machinery”, the author expresses the idea that man is _________.| A．an essential part of society with irreplaceable functions |
| B．expected to work in reasonable harmony with the rest of society |
| C．an unimportant component of society, though functioning smoothly |
| D．responsible for the smooth running of society and business operations |
| A．they are filled with an overwhelming fear of being unemployed |
| B．they don’t have any genuine satisfaction or interest |
| C．they have to face the fundamental realities of human existence |
| D．they lack a sense of independence and productivity |
| A．caution | B．obedience | C．commitment | D．optimism |
| A．To introduce the production mode of our ancestors. |
| B．To show the problematic situation in society. |
| C．To argue for full development of human potentials. |
| D．To help people escape production and consumption. |
5 . Early fifth-century philosopher St. Augustine famously wrote that he knew what time was unless someone asked him. Albert Einstein added another wrinkle when he theorized that time varies depending on where you measure it. Today’s state-of-the-art atomic (原子的) clocks have proven Einstein right. Even advanced physics can’t decisively tell us what time is, because the answer depends on the question you’re asking.
Forget about time as an absolute. What if, instead of considering time in terms of astronomy, we related time to ecology? What if we allowed environmental conditions to set the tempo (节奏) of human life? We’re increasingly aware of the fact that we can’t control Earth systems with engineering alone, and realizing that we need to moderate (调节) our actions if we hope to live in balance. What if our definition of time reflected that?
Recently, I conceptualized a new approach to timekeeping that’s connected to circumstances on our planet，conditions that might change as a result of global warming. We’re now building a clock at the Anchorage Museum that reflects the total flow of several major Alaskan rivers, which are sensitive to local and global environmental changes. We’ve programmed it to match an atomic clock if the waterways continue to flow at their present rate. If the rivers run faster in the future on average, the clock will get ahead of standard time. If they run slower, you’ll see the opposite effect.
The clock registers both short-term irregularities and long-term trends in river dynamics. It’s a sort of observatory that reveals how the rivers are behaving from their own temporal frame (时间框架), and allows us to witness those changes on our smartwatches or phones. Anyone who opts to go on Alaska Mean River Time will live in harmony with the planet. Anyone who considers river time in relation to atomic time will encounter a major imbalance and may be motivated to counteract it by consuming less fuel or supporting greener policies.
Even if this method of timekeeping is novel in its particulars，early agricultural societies also connected time to natural phenomena. In pre-Classical Greece, for instance, people “corrected” official calendars by shifting dates forward or backward to reflect the change of season. Temporal connection to the environment was vital to their survival. Likewise, river time and other timekeeping systems we’re developing may encourage environmental awareness.
When St. Augustine admitted his inability to define time, he highlighted one of time’s most noticeable qualities: Time becomes meaningful only in a defined context. Any timekeeping system is valid, and each is as praiseworthy as its purpose.
The author raises three questions in Paragraph 2 mainly to________.| A．present an assumption | B．evaluate an argument |
| C．highlight an experiment | D．introduce an approach |
6 . In the annals of human history, few subjects have generated as much excitement, debate, and guess as artificial intelligence (AI). This revolutionary technology, which enables machines to perform tasks that once required human intelligence, has the potential to transform every part of our society, from healthcare and finance to transportation and entertainment.
At its heart, AI is all about data. Massive amounts of data are fed into algorithms that learn from this data, allowing them to make predictions, recognize patterns, and even make decisions. This “machine learning” is the driving force behind many of the AI applications we see today, from virtual assistants like Siri and Alexa to more advanced systems like IBM’s Watson, which can analyze vast amounts of information to assist doctors in diagnosing diseases.
The transformative potential of AI is undeniable. In the medical field, for instance, AI can assist in early detection of diseases, predict patient outcomes, and even suggest treatment options. In finance, algorithms can predict stock market trends, and provide personalized financial advice. In transportation, self-driving cars equipped with AI systems promise to reduce accidents, ease traffic jams, and transform urban landscapes.
However, with great potential comes great responsibility. The rise of AI has caused debates about is ethical implications (道德含义). The machines are only as good as the data they are fed, and there’s a growing concern about biases (偏见) being built into AI systems. For instance, facial recognition technologies, used in everything from unlocking phones to police monitoring cameras, have come under check for misidentifying individuals based on race or gender.
Moreover, the widespread adoption of AI could lead to significant job displacement. While new roles and industries might emerge as a result of AI, it is not sure that these will pay off the jobs lost. This could increase income inequalities and causes difficulties to social systems.
Another major concern is the “black box” nature of AI. Many AI systems operate in ways that even their creators don’t fully understand. This can be problematic, especially in critical applications like healthcare or criminal justice where understanding the logic behind a decision is important.
Then there’s the potential for AI to be weaponized. In the hands of evil actors, AI could be used to spread misinformation, control public opinion, or even engage in internet warfare. The global community must come together to set standards and regulations to prevent such misuse.
On the brighter side, many experts believe that by setting the right frameworks and investing in education and retraining, we can use the power of AI for the greater good. By fostering (促进) a culture of continuous learning and staying abreast (并排的，并肩的) of technological advancements, society can benefit from the promise of AI while avoiding its potential dangers.
In conclusion, artificial intelligence stands as one of the most profound inventions of our time. While it offers vast opportunities, it also poses significant challenges that we, as a society, must welcome. As we stand at this technological crossroads, our choices will determine whether AI serves as a benefit or a harm for humanity.
1. Which of the following best describes the method by which machines acquire the capability to perform tasks that traditionally required human intelligence?| A．By programming predefined rules. |
| B．Through user interactions every day. |
| C．By ingesting and processing vast amounts of data. |
| D．Via regular software updates from developers. |
| A．By citing numerous statistical data. |
| B．By presenting both the positive potential and the challenges of AI. |
| C．Through personal experiences. |
| D．By focusing on the negative effects of AI. |
| A．The Rise of Virtual Assistants: Siri and Alexa |
| B．Understanding the Mechanisms Behind AI Algorithms |
| C．Artificial Intelligence: Charting the Course for Tomorrow’s Tech |
| D．Balancing the Potential and challenges of AI in Modern Society |
| A．AI has already replaced most human jobs and is the leading cause of unemployment. |
| B．The global community has taken measures to prevent AI misuse. |
| C．The operation of many AI systems is easily understood by their creators. |
| D．The solving to the dilemma brought by AI needs collective efforts of our society. |
7 . Think of the words in your head: that tasteless joke you wisely kept to yourself at dinner; your unvoiced impression of your best friend’s new partner. Now imagine that someone could listen in.
Recently, scientists from the University of Texas, have made another step in that direction. In a study published in Neuroscience, the team showed it was possible to read people’s thoughts with a non-invasive brain scanner called fMRI and large language models (LLMs) built with GPT.
The study centered on three subjects, who lay in an fMRI scanner recording their brain activity by detecting changes in blood flow in parts of their brains while they listened to online stories. By integrating this information with the LLMs’ ability to understand how words relate to one another, the researchers developed an encoded (编码的) map of how each individual’s brain responds to different words. Then, the team worked backward. They recorded the fMRI activity while the participants listened to a new story. Using a combination of the patterns previously encoded for each individual and LLMs, the researchers attempted to translate this new brain activity.
While many of the sentences it produced were inaccurate, the decoder generated sentences that got the main idea of what the person was thinking. For instance, when a person heard, “I don’t have my driver’s license yet,” the decoder spat out, “She has not even started to learn to drive yet.” Alex Huth from the university said, “We were shocked and impressed that this worked as well as it does.”
The researchers also found that the technology isn’t one-size-fits-all. Each decoder was quite personalized and worked only for the person whose brain data had helped build it. Additionally, a person had to voluntarily cooperate for the decoder to identify ideas. If a person wasn’t paying attention to an audio story, the decoder couldn’t pick that story up from brain signals.
While the technology was still far from perfect, the result could ultimately lead to seamless devices that help people who can’t talk or otherwise communicate easily. However, the research also raises privacy concerns about unwelcome neural overhearing. The team said the potential of the technology was such that policymakers should proactively address how it can be legally used. Jerry Tang from the team said, “Nobody’s brain should be decoded without their permission. If one day it does become possible to get accurate decoding without a person’s will, we’ll have a regulatory foundation in place.”
1. What is the study mainly about?| A．The working principle of a smart scanner. |
| B．The potential impact of mind-reading GPT. |
| C．The advance in brain-decoding technology. |
| D．The breakthrough in large language models. |
| A．They fed the decoder data on people’s brain activities. |
| B．They employed the scanner to encode people’s thoughts. |
| C．They recorded the fMRI activity to assess thinking ability. |
| D．They used brain activity patterns to read the subjects’ mind. |
| A．The decoder worked as expected. |
| B．The decoder can get the wording right. |
| C．The decoder required willing participation. |
| D．The decoder can be applied to different people. |
| A．Personalize the technology. | B．Establish proper regulations. |
| C．Apply the technology across fields. | D．Break limitations of the technology. |
8 . The need for clarity extends beyond how we communicate science to how we evaluate it. Who can really define stock phrases such as ‘a significant contribution to research’? Or understand what ‘high impact’ or ‘world-class’ mean? Scientists demand that institutions should be clear about their criteria and consider all scholarly outputs—preprints, code, data, peer review, teaching, mentoring and so on.
My view about the practices in research assessment is that most assessment guidelines permit sliding standards: instead of clearly defined terms, they give us feel-good slogans that lack any fixed meaning. Facing the problem will get us much of the way towards a solution.
Broad language increases room for misunderstanding. ‘High impact’ can be code for where research is published. Or it can mean the effect that research has had on its field, or on society locally or globally—often very different things. Yet confusion is the least of the problems. Words such as ‘world-class’ and ‘excellent’ allow assessors to vary comparisons depending on whose work they are assessing. Academia(学术界) cannot be a fair and reasonable system if standards change depending on whom we are evaluating. Unconscious bias(偏见) associated with factors such as a researcher’s gender, ethnic origin and social background helps the academic injustice continue. It was only with double-blind review of research proposals that women finally got fair access to the Hubble Space Telescope.
Many strategies exist to improve fairness in academia, but conceptual clarity is paramount. Being clear about how specific qualities are valued leads assessors to think critically about whether those qualities are truly being considered. Achieving that conceptual clarity requires discussion with faculties, staff and students: hours and hours of it. The University Medical Center Utrecht in the Netherlands, for example, held a series of conversations, each involving 20-60 researchers, and then spent another year revising its research assessment policies to recognize social impacts.
Frank conversations about what is valued in a particular context, or at a specific institution, are an essential first step in developing concrete recommendations. Although ambiguous(模棱两可的) terms, for instance ‘world-class’ and ‘significant’, are a barrier when performing assessments, university administrators have said that they rely on flexible language to make room to reward a variety of contributions. So it makes sense that more specific language in review and promotion must be able to accommodate varied outputs, outcomes and impacts of scholarly work.
Setting specific standards will be tough. It will be inviting to fall back on the misleading standards such as impact factors, or on ambiguous terms that can be agreed to by everyone but applied wisely by no one. It is too early to know what those standards will be or how much they will vary, but the right discussions are starting to happen. They must continue.
1. Regarding the current practices in research assessment, the author is ________.| A．supportive | B．puzzled |
| C．unconcerned | D．disapproving |
| A．Bias on assessors can cause inequality. | B．Frank conversations harm scholarly work. |
| C．Specific qualities need to be clearly stated. | D．Broad language ensures academic fairness. |
| A．primary. | B．recognized. |
| C．optional. | D．accomplished. |
| A．Fix research assessment. Change slogans for clear standards. |
| B．Fix research assessment. Change evaluations for conversations. |
| C．Define research assessment. Change simplicity for specification. |
| D．Define research assessment. Change broad language for flexible one. |
9 . In the story A Christmas Carol, the wealthy miser (吝啬鬼) Ebenezer Scrooge has a magical, life-changing epiphany (顿悟). Scrooge’s eyes are opened as to how his behavior affects other people — and he goes from a selfish grump to a generous benefactor overnight, thanks to visits from ghosts.
Scrooge’s transformation comes down to knowledge. But do people really want to know how their actions affect others? As behavioral scientists, we wanted to understand just how common willful ignorance is — as well as why people engage in it.
Experiments were carried out to find answers. Researchers asked one member of each pair to choose between two options (选择) in one of two settings, determining the earnings for themselves and their partner.
In the transparent setting, if they chose $5 for themselves, they knew their partner would also receive $5. If, however, they chose $6 for themselves, they knew their partner would receive only $1 in return.
In the ambiguous setting, there were two possible situations. In one, if the decision-maker selected $6 for themselves, their partner would receive $1, and if the decision-maker chose $5, their partner would receive $5. But in the other situation, the decision-maker could pick $6 and their partner would receive $5, or the decision-maker could select $5 and their partner would receive $1. The decision-maker knew these two systems — but they were not initially aware of which situation they were in. Interestingly, the decision-maker had the opportunity to resolve that ambiguity by clicking a button.
Across all studies, we found in the transparent setting 55% chose the altruistic option. In the ambiguous setting, however, 40% of participants chose to remain ignorant. 60% of people in the ignorant group chose a higher personal payout in situations where this choice came at the expense of their partner. Among those who requested more information, 36% knowingly kept a higher payout at a cost to their partner. Only 39% of people in the ambiguous setting made the choice that ultimately benefited their partner — a significant drop from 55% in the transparent condition.
But how do we know if ignorance in the ambiguous setting was willful? We conducted a second analysis focused on what motivates people to seek information.
In this analysis we looked at how people who obtained additional information behaved in comparison with those who were given information. We found that people who chose to receive information in the ambiguous setting were seven percentage points more likely to make the altruistic choice than people in the transparent setting. By the same token, the finding also suggests ignorance prevents people from knowing how their actions harm others.
If we can avoid putting a strong moral emphasis on decisions, it may make people feel less threatened and, as a result, less willfully ignorant. We may not have Dickensian ghosts to guide us — but there are still steps we can take.
1. The author mentions Scrooge’s change mainly to ______.| A．draw a comparison | B．introduce a topic |
| C．evaluate a character | D．give an example |
| A．drop out of the experiment | B．know the situation they are in |
| C．receive the additional earnings | D．switch to the other situation they prefer |
| A．Inadvisable. | B．Selfless. | C．Fair-minded. | D．Unrealistic. |
| A．The ignorant group tend to sacrifice their own interest. |
| B．Moral evaluation might lead to more intentional ignorance. |
| C．There is no common payout system shared by both settings. |
| D．Avoiding information might make people feel like bad persons. |
10 . Despite decades of research, disorders of the brain have proved especially difficult to treat. There is schizophrenia (精神分裂症), which has not seen a breakthrough for more than 60 years, since the discovery of chlorpromazine — which happened largely by chance. But the story of chlorpromazine offers a powerful lesson: originally an antihistamine (抗过敏药), it was repurposed as a medicine for schizophrenia.
As a scientist who has studied schizophrenia for decades, I am convinced that we could have similar successes with other medicines already on our shelves. Because an existing drug has already passed Food and Drug Administration tests(FDA-approved), successfully repurposing it could take less than half of the estimated 13 years and significantly less than the average $2-billion to $3-billion cost of developing a single drug from nothing.
The thousands of FDA-approved drugs thus represent a vast resource that can possibly be adapted to target any number of conditions. But this possibility is largely unexplored, in part because drug companies always have to restructure their Research and Development (R&D) programs to look at other diseases. There are also thousands of drugs that are not FDA-approved. When a company discontinues development of a drug, whatever researchers know is locked up in that company’s files and might as well be lost.
Scientists need access (使用机会) to this information. If this information could be directed into a centralized resource, it would be great news. Researchers could employ the latest tools in bio-informatics, data science and machine learning to uncover common molecular (分子的) themes among or between diseases and promising drugs. Yet many drug companies are still unwilling to reveal anything that might put their copyrights at risk. Even academics may hesitate to share with competing laboratories.
To cope with this, organizations like the FDA must develop motivations for sharing data, such as by creating legal safeguards for privacy and commercial interests. These motivations could then open the floodgates for easy-to-use, open platforms for efficiently sharing and mining data. This would not have been possible five years ago. But now is a critical moment, and we have never been closer to real breakthroughs.
In my lab, we are testing certain cancer drugs that restore some of the biological processes that are disturbed in schizophrenia. We want to see if the drugs have the same restorative features in the brain cells of schizophrenia patients. This is a proof of the idea that a systematic and strategic approach to drug repurposing could actually move the needle. There is no time to waste. What we need is cooperation from drug companies and academic scientists alike — and access to the lifesaving data they hold.
1. Why does the author mention chlorpromazine in the first paragraph?| A．To stress the difficulty in treating brain disorders. |
| B．To explain medical progress could happen by luck. |
| C．To introduce a medicine breakthrough in medical history |
| D．To show a medicine for a certain illness can treat another disease. |
| A．Information arising from drug development can be wasted. |
| B．The undeveloped functions of present medicines are overvalued. |
| C．We should treasure FDA-approved drugs more than the unapproved. |
| D．Studying existing drugs is more likely to succeed than developing new ones. |
| A．supportive | B．negative | C．understanding | D．uncertain |
| A．New Drugs from Old | B．Access to Lifesaving Data |
| C．Between Drug Companies and Scientists. | D．Before and After Medical Breakthroughs |
